We are pleased to announce that the Audio Analysis Lab will be giving a tutorial this year at Interspeech 2017. The tutorial is entitled Statistical Parametric Speech Processing: Solving Problems with the Model-based Approach and covers much of the lab’s research in present and past projects. Interspeech 2017 will be held in beautiful Stockholm, Sweden August 20-24, and the tutorial will be held 9:30-12:00 on August 20. The tutorial will be given by Assistant Professor Jesper Rindom Jensen, Assistant Professor Jesper Kjær Nielsen, and Professor Mads Græsbøll Christensen. You can read more about the tutorial and the other tutorials here and you can sign up at the Interspeech homepage here once the registration opens. Below, you can also find additional information about the tutorial.

Title: Statistical Parametric Speech Processing: Solving Problems with the Model-based Approach

Organizers: Jesper Rindome Jensen, Jesper Kjær Nielsen, and Mads Græsbøll Christensen

Abstract: Parametric speech models have been around for many years but have always had their detractors. Two common arguments against such models are that it is too difficult to find their parameters and that the models do not take the complicated nature of real signals into account. In recent years, significant advances have been made in speech models and robust and computationally efficient estimation using statistical principles, and it has been demonstrated that, regardless of any deficiencies in the model, the parametric methods outperform the more commonly used non-parametric methods (e.g., autocorrelation-based methods) for problems like pitch estimation. The application of these principles, however, extend way beyond that problem. In this tutorial, state-of-the-art parametric speech models and statistical estimators for finding their parameters will be presented and their pros and cons discussed. The merits of the statistical, parametric approach to speech modeling will be demonstrated via a number of number of well-known problems in speech, audio and acoustic signal processing. Examples of such problems are pitch estimation for non-stationary speech, distortion-less speech enhancement, noise statistics estimation, speech segmentation, multi-channel modeling, and model-based localization and beamforming with microphone arrays.